Vignette Title

Multi-category dot-density maps often work well when the categories cluster geographically. Recent immigrants by country of origin work well for this. ## Data First we grab the immigrant data via cancensus, making use of the CensusMapper API tool to select the regions and variables we need.

#devtools::install_github("mountainmath/cancensus")
library(cancensus)
library(dotdensity)
# options(cancensus.api_key='your_api_key')
regions=list(CMA="59933")
vectors=c("v_CA11N_265","v_CA11N_268","v_CA11N_304","v_CA11N_334","v_CA11N_373","v_CA11N_376","v_CA11N_379","v_CA11N_382")

We choose the categories and colours we want to map and define a convenience function to rename the variables and compute the qantities for the other asian countries that we don’t break out.

categories=c("Americas","Europe","Africa","Philippines","China","India","Other Asian Countries")
colors=c("#7a0177", "#3333cc", "#ff00ff", "#00ffff", "#ff1a1c", "#4dff4a", "#ffff33")
prep_data <- function(geo){
  data <- geo@data %>% replace(is.na(.), 0)
  data <- rename(data,
    total=v_CA11N_265,
    Americas=v_CA11N_268,                                  
    Europe=v_CA11N_304,
    Africa=v_CA11N_334,
    Philippines=v_CA11N_376,
    China=v_CA11N_379,
    India=v_CA11N_382)
  data <- mutate(data,`Other Asian Countries` = v_CA11N_373-Philippines-China-India)
  geo@data <- data
  return(geo)
}

Next we grab the data via cancensus,

data_csd=get_census(dataset = 'CA11', regions=regions,vectors=vectors,geo_format='sp',labels='short',level='CSD') %>% prep_data

OGR data source with driver: GeoJSON 
Source: "data_cache/CM_geo_5944ecb0017405460024293a4e1245b4.geojson", layer: "OGRGeoJSON"
with 39 features
It has 12 fields

data_ct=get_census(dataset = 'CA11', regions=regions,vectors=vectors,geo_format='sp',labels='short',level='CT') %>% prep_data

OGR data source with driver: GeoJSON 
Source: "data_cache/CM_geo_2c2ba582c4fbc552cd7375e1cdb94585.geojson", layer: "OGRGeoJSON"
with 457 features
It has 12 fields

data_da=get_census(dataset = 'CA11', regions=regions,vectors=vectors,geo_format='sp',labels='short',level='DA') %>% prep_data

OGR data source with driver: GeoJSON 
Source: "data_cache/CM_geo_6b5f32d4a2ad20b88dbcddc4dc419562.geojson", layer: "OGRGeoJSON"
with 3438 features
It has 12 fields

data_db=get_census(dataset = 'CA11', regions=regions,geo_format='sp',labels='short',level='DB')

OGR data source with driver: GeoJSON 
Source: "data_cache/CM_geo_1cc7cbd8f3815230304992d45a56ef3b.geojson", layer: "OGRGeoJSON"
with 15479 features
It has 10 fields

which we then re-aggregate to make sure we don’t miss overall counts due to privacy cutoffs distribute them proportionally among the population.

data_ct@data <- dot_density.proportional_re_aggregate(data=data_ct@data,parent_data=data_csd@data,geo_match=setNames("GeoUID","CSD_UID"),categories=categories,base="Population")
data_da@data <- dot_density.proportional_re_aggregate(data=data_da@data,parent_data=data_ct@data,geo_match=setNames("GeoUID","CT_UID"),categories=categories,base="Population")
data_db@data <- dot_density.proportional_re_aggregate(data=data_db@data,parent_data=data_da@data,geo_match=setNames("GeoUID","DA_UID"),categories=categories,base="Population")

Map

All that’s left to do is to covert our re-aggregated block-level data to dots, using the dot_density.compute_dots function from the dotdensity package and feed it into the dot_density.dots_map function to add them to our basemap.

# 1 dot = 5 immigrants
scale=5
dots.db <- dot_density.compute_dots(geo_data = data_db, categories = categories, scale=scale)
basemap +
# shade unpopulated blocks
#  geom_polygon(data=data_db[data_db$Population<=5,], 
#                        aes(long, lat, group = group), 
#                        fill = "#222222", size=0.1, 
#                        color = "#222222") +
  scale_colour_manual(values = colors) +
  labs(color = "",
                title="Immigrants 2006 - 2011",
                caption="Source: StatCan Census 2016 via cancensus & CensusMapper.ca",
                subtitle = paste0("1 dot = ",scale," people")) + 
  dot_density.dots_map(dots=dots.db,alpha=0.75,size=0.25)

# save image for later
ggsave('../images/recent_immigrants.png',width=26,height=26)